# Computational Anatomy for Multi-Organ Analysis in Medical Imaging: A Review

## TL;DR

This review discusses the evolution and current state of multi-organ computational anatomy models in medical imaging, emphasizing their ability to incorporate inter-organ relations for improved anatomical and functional understanding.

## Contribution

It provides a comprehensive classification and analysis of techniques from traditional models to modern deep learning approaches for multi-organ analysis.

## Key findings

- Growth in multi-organ modeling approaches over time
- Deep learning methods are increasingly used for multi-organ analysis
- Multi-organ models enhance accuracy and clinical relevance

## Abstract

The medical image analysis field has traditionally been focused on the development of organ-, and disease-specific methods. Recently, the interest in the development of more 20 comprehensive computational anatomical models has grown, leading to the creation of multi-organ models. Multi-organ approaches, unlike traditional organ-specific strategies, incorporate inter-organ relations into the model, thus leading to a more accurate representation of the complex human anatomy. Inter-organ relations are not only spatial, but also functional and physiological. Over the years, the strategies 25 proposed to efficiently model multi-organ structures have evolved from the simple global modeling, to more sophisticated approaches such as sequential, hierarchical, or machine learning-based models. In this paper, we present a review of the state of the art on multi-organ analysis and associated computation anatomy methodology. The manuscript follows a methodology-based classification of the different techniques 30 available for the analysis of multi-organs and multi-anatomical structures, from techniques using point distribution models to the most recent deep learning-based approaches. With more than 300 papers included in this review, we reflect on the trends and challenges of the field of computational anatomy, the particularities of each anatomical region, and the potential of multi-organ analysis to increase the impact of 35 medical imaging applications on the future of healthcare.

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Source: https://tomesphere.com/paper/1812.08577